Cameras and image-editing software often process images in the wide-gamut
ProPhoto color space, encompassing 90% of all visible colors. However, when
images are encoded for sharing, this color-rich representation is transformed
and clipped to fit within the small-gamut standard RGB (sRGB) color space,
representing only 30% of visible colors. Recovering the lost color information
is challenging due to the clipping procedure. Inspired by neural implicit
representations for 2D images, we propose a method that optimizes a lightweight
multi-layer-perceptron (MLP) model during the gamut reduction step to predict
the clipped values. GamutMLP takes approximately 2 seconds to optimize and
requires only 23 KB of storage. The small memory footprint allows our GamutMLP
model to be saved as metadata in the sRGB image -- the model can be extracted
when needed to restore wide-gamut color values. We demonstrate the
effectiveness of our approach for color recovery and compare it with
alternative strategies, including pre-trained DNN-based gamut expansion
networks and other implicit neural representation methods. As part of this
effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut
images for training and testing. Our code and dataset can be found on the
project website: this https URL

使用多层感知器模型，在色域缩减时在线学习预测被剪裁颜色的值，并在 sRGB 图像元数据中保存该模型以恢复宽域颜色值的有效性，对比曾经的解决方法。在自家构建的 2,200 张图像数据集上进行了评估。